Photocatalytic materials are being investigated as effective bactericides due to their superior ability to inactivate a broad range of dangerous microbes. In this study, the following two types of bacteria were employed for bactericidal purposes: Gram-negative Escherichia coli (E. coli) and Gram-positive Staphylococcus aureus (S. aureus). The shape, crystal structure, element percentage, and optical properties of Ag9(SiO4)2NO3 were examined after it was successfully synthesized by a standard mixing and grinding processing route. Bactericidal efficiency was recorded at 100% by the following two types of light sources: solar and simulated light, with initial photocatalyst concentration of 2 µg/mL, and 97% and 95% of bactericidal activity in ultra-low photocatalyst concentration of 0.2 µg/mL by solar and simulated light, respectively, after 10 min. The survival rate was studied for 6 min, resulting in 99.8% inhibition at the photocatalyst dose of 2 µg/mL. The mechanism of bactericidal efficiency was found to be that the photocatalyst has high oxidation potential in the valence band. Consequently, holes play a significant part in bactericidal efficiency.
The aim of the research is to determine the requirements for developing the technical capabilities of the agricultural extension service providers to face the effects of climatic changes in Baghdad Governorate, to achieve the goal of the research and in order to obtain the respondents’ approval of the requirements (28) requirements were identified in the light of the literature and studies related to the subject and the opinions of specialists to develop the technical capabilities of the agricultural extension service providers distributed on two axes (the ability to know the effects of climate changes, the ability to know the practices to reduce the effects of climate changes). The
Prediction of daily rainfall is important for flood forecasting, reservoir operation, and many other hydrological applications. The artificial intelligence (AI) algorithm is generally used for stochastic forecasting rainfall which is not capable to simulate unseen extreme rainfall events which become common due to climate change. A new model is developed in this study for prediction of daily rainfall for different lead times based on sea level pressure (SLP) which is physically related to rainfall on land and thus able to predict unseen rainfall events. Daily rainfall of east coast of Peninsular Malaysia (PM) was predicted using SLP data over the climate domain. Five advanced AI algorithms such as extreme learning machine (ELM), Bay
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